'''simple docstring'''




from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


__UpperCAmelCase					=logging.get_logger(__name__)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       Tuple:
    try:
        with open(UpperCamelCase__							,    '''rb'''							) as flax_state_f:
            __lowerCamelCase            =    from_bytes(UpperCamelCase__							,    flax_state_f.read()							)
    except UnpicklingError as e:
        try:
            with open(UpperCamelCase__							) as f:
                if f.read().startswith('''version'''							):
                    raise OSError(
                        '''You seem to have cloned a repository without having git-lfs installed. Please'''
                        ''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
                        ''' folder you cloned.'''							)
                else:
                    raise ValueError from e
        except (UnicodeDecodeError, ValueError):
            raise EnvironmentError(f"""Unable to convert {model_file} to Flax deserializable object. """							)

    return load_flax_weights_in_pytorch_model(UpperCamelCase__							,    UpperCamelCase__							)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       Dict:

    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            '''Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'''
            ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
            ''' instructions.'''							)
        raise

    # check if we have bf16 weights
    __lowerCamelCase            =    flatten_dict(jax.tree_util.tree_map(lambda UpperCamelCase__							: x.dtype == jnp.bfloataa							,    UpperCamelCase__							)							).values()
    if any(UpperCamelCase__							):
        # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

        # and bf16 is not fully supported in PT yet.
        logger.warning(
            '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
            '''before loading those in PyTorch model.'''							)
        __lowerCamelCase            =    jax.tree_util.tree_map(
            lambda UpperCamelCase__							: params.astype(np.floataa							) if params.dtype == jnp.bfloataa else params							,    UpperCamelCase__							)

    __lowerCamelCase            =    ''''''

    __lowerCamelCase            =    flatten_dict(UpperCamelCase__							,    sep='''.'''							)
    __lowerCamelCase            =    pt_model.state_dict()

    # keep track of unexpected & missing keys
    __lowerCamelCase            =    []
    __lowerCamelCase            =    set(pt_model_dict.keys()							)

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        __lowerCamelCase            =    flax_key_tuple.split('''.'''							)

        if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
            __lowerCamelCase            =    flax_key_tuple_array[:-1] + ['''weight''']
            __lowerCamelCase            =    jnp.transpose(UpperCamelCase__							,    (3, 2, 0, 1)							)
        elif flax_key_tuple_array[-1] == "kernel":
            __lowerCamelCase            =    flax_key_tuple_array[:-1] + ['''weight''']
            __lowerCamelCase            =    flax_tensor.T
        elif flax_key_tuple_array[-1] == "scale":
            __lowerCamelCase            =    flax_key_tuple_array[:-1] + ['''weight''']

        if "time_embedding" not in flax_key_tuple_array:
            for i, flax_key_tuple_string in enumerate(UpperCamelCase__							):
                __lowerCamelCase            =    (
                    flax_key_tuple_string.replace('''_0'''							,    '''.0'''							)
                    .replace('''_1'''							,    '''.1'''							)
                    .replace('''_2'''							,    '''.2'''							)
                    .replace('''_3'''							,    '''.3'''							)
                    .replace('''_4'''							,    '''.4'''							)
                    .replace('''_5'''							,    '''.5'''							)
                    .replace('''_6'''							,    '''.6'''							)
                    .replace('''_7'''							,    '''.7'''							)
                    .replace('''_8'''							,    '''.8'''							)
                    .replace('''_9'''							,    '''.9'''							)
                )

        __lowerCamelCase            =    '''.'''.join(UpperCamelCase__							)

        if flax_key in pt_model_dict:
            if flax_tensor.shape != pt_model_dict[flax_key].shape:
                raise ValueError(
                    f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
                    f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""							)
            else:
                # add weight to pytorch dict
                __lowerCamelCase            =    np.asarray(UpperCamelCase__							) if not isinstance(UpperCamelCase__							,    np.ndarray							) else flax_tensor
                __lowerCamelCase            =    torch.from_numpy(UpperCamelCase__							)
                # remove from missing keys
                missing_keys.remove(UpperCamelCase__							)
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(UpperCamelCase__							)

    pt_model.load_state_dict(UpperCamelCase__							)

    # re-transform missing_keys to list
    __lowerCamelCase            =    list(UpperCamelCase__							)

    if len(UpperCamelCase__							) > 0:
        logger.warning(
            '''Some weights of the Flax model were not used when initializing the PyTorch model'''
            f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
            f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
            ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
            f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
            ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
            ''' FlaxBertForSequenceClassification model).'''							)
    if len(UpperCamelCase__							) > 0:
        logger.warning(
            f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
            f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
            ''' use it for predictions and inference.'''							)

    return pt_model

'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCAmelCase					=logging.get_logger(__name__)

__UpperCAmelCase					={
    "facebook/levit-128S": "https://huggingface.co/facebook/levit-128S/resolve/main/config.json",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Optional[Any]				="levit"






     def __init__(					self						:						Optional[int]    ,							a						:						int=2_24    ,							a						:						Union[str, Any]=3    ,							a						:						List[Any]=3    ,							a						:						Dict=2    ,							a						:						Tuple=1    ,							a						:						Optional[int]=16    ,							a						:						Optional[Any]=[1_28, 2_56, 3_84]    ,							a						:						Dict=[4, 8, 12]    ,							a						:						Optional[Any]=[4, 4, 4]    ,							a						:						Optional[int]=[16, 16, 16]    ,							a						:						Any=0    ,							a						:						List[Any]=[2, 2, 2]    ,							a						:						Optional[Any]=[2, 2, 2]    ,							a						:						Union[str, Any]=0.02    ,							**a						:						Optional[int]    ,							):





         """simple docstring"""



         super().__init__(**a       )
         __lowerCamelCase            =    image_size
         __lowerCamelCase            =    num_channels
         __lowerCamelCase            =    kernel_size
         __lowerCamelCase            =    stride
         __lowerCamelCase            =    padding
         __lowerCamelCase            =    hidden_sizes
         __lowerCamelCase            =    num_attention_heads
         __lowerCamelCase            =    depths
         __lowerCamelCase            =    key_dim
         __lowerCamelCase            =    drop_path_rate
         __lowerCamelCase            =    patch_size
         __lowerCamelCase            =    attention_ratio
         __lowerCamelCase            =    mlp_ratio
         __lowerCamelCase            =    initializer_range
         __lowerCamelCase            =    [
             ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
             ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
         ]




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Tuple				=version.parse("1.11"					)






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]       )






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]       ):





         """simple docstring"""



         return 1e-4
